发布时间：

2026-06-30

所属单位：

人工智能教育学部

论文名称：

Rotation Invariance for Offline Handwritten Chemical Organic Ring Structure Symbols Recognition

发表刊物：

IEEE TALE2021: IEEE INTERNATIONAL CONFERENCE ON ENGINEERING, TECHNOLOGY AND EDUCATION

刊物所在地：

国内外公开发行

关键字：

Chemical organic ring structure symbols; Handwritten symbols recognition; Rotation invariance

摘要：

Auto grading is an instruction tool which could reduce teachers' burden, provide students with instant feedback and support highly personalized learning. One key technique is to recognize students' handwritten assignments. This work will be focused on the task of recognizing handwritten chemical organic ring structure symbols which exhibit the characteristic of rotational symmetry. Due to the global parameter sharing mechanism and pooling operation, convolutional neural networks (CNNs) have the power to learn translation-invariance features. However, the design of the standard CNN itself does not specifically consider the rotation invariance. In this paper, we explore different methods to improve the property of rotation invariance of CNN and apply them for semantic recognition of handwritten chemical organic ring structure symbols. The first one is a test-time augmentation method with voting strategy without modifications to the network architecture. For the second method, we add 2 new layers into the model to endow it with the property of rotation invariance. These 2 proposed methods are evaluated on a self-collected data set and achieve the recognition accuracy of 98.75% and 93.125% respectively.

第一作者：

张婷

论文类型：

会议论文

通讯作者：

Du, Shiyi,余新国,周健

论文编号：

1575761594019876864

文献类型：

00013

页面范围：

844-848

是否译文：

否

发表时间：

2021-12-08

收录刊物：

其他（ESI、Scopus、OA）、EI